Mobile robot localization in quasi‐dynamic environments
Bibliographic record
Abstract
Purpose The purpose of this paper is to address the online localization of mobile (service) robots in real world dynamic environments. Most of the techniques developed so far have been designed for static environments. What is presented here is a novel technique for mobile robot localization in quasi‐dynamic environments. Design/methodology/approach The proposed approach employs a probability grid map and Baye's filtering techniques. The former is used for representing the possible changes in the surrounding environment which a robot might have to face. Findings Simulation and experimental results show that this approach has a high degree of robustness by taking into account both sensor and world uncertainty. The methodology has been tested under different environment scenarios where diverse complex objects having different sizes and shapes were used to represent movable and non‐movable entities. Practical implications The results can be applied to diverse robotic systems that need to move in changing indoor environments such as hospitals and places where people might require assistance from autonomous robotic devices. The methodology is fast, efficient and can be used in fast‐moving robots, allowing them to perform complex operations such as path planning and navigation in real time. Originality/value What is proposed here is a novel mobile robot localization approach that enables unmanned vehicles to effectively move in real time and know their current location in dynamic environments. Such an approach consists of two steps: a generation of the probability grid map; and a recursive position estimation methodology employing a variant of the Baye's filter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".